The challenge of scale
Deploying AI agents at enterprise scale presents unique architectural challenges. Unlike traditional web applications, agent systems must handle variable-length conversations, maintain state across interactions, and orchestrate calls to multiple external services.
Core architecture patterns
#Event-driven orchestration
Agent systems benefit from event-driven architectures that decouple components and enable horizontal scaling. Each agent interaction generates events that can be processed asynchronously, allowing the system to handle burst traffic gracefully.
#Stateful session management
Maintaining conversation context requires careful thought about state management. Options include:
• In-memory caching with Redis or similar technologies
• Persistent storage for long-running conversations
• Hybrid approaches that balance performance with durability
#Model serving infrastructure
Serving LLMs at scale requires specialized infrastructure:
• GPU clusters with efficient batch processing
• Model caching and warm-up strategies
• Automatic scaling based on inference latency
Reliability considerations
Enterprise deployments must account for:
1. Graceful degradation: When AI systems fail, fallback to human agents or simpler automated responses
2. Circuit breakers: Prevent cascading failures when external services become unavailable
3. Comprehensive observability: Tracing, metrics, and logging for debugging production issues
#
Event-driven orchestration
Agent systems benefit from event-driven architectures that decouple components and enable horizontal scaling. Each agent interaction generates events that can be processed asynchronously, allowing the system to handle burst traffic gracefully.
#Stateful session management
Maintaining conversation context requires careful thought about state management. Options include:
• In-memory caching with Redis or similar technologies
• Persistent storage for long-running conversations
• Hybrid approaches that balance performance with durability
#Model serving infrastructure
Serving LLMs at scale requires specialized infrastructure:
• GPU clusters with efficient batch processing
• Model caching and warm-up strategies
• Automatic scaling based on inference latency
Reliability considerations
Enterprise deployments must account for:
1. Graceful degradation: When AI systems fail, fallback to human agents or simpler automated responses
2. Circuit breakers: Prevent cascading failures when external services become unavailable
3. Comprehensive observability: Tracing, metrics, and logging for debugging production issues
Maintaining conversation context requires careful thought about state management. Options include:
• In-memory caching with Redis or similar technologies
• Persistent storage for long-running conversations
• Hybrid approaches that balance performance with durability
#
Model serving infrastructure
Serving LLMs at scale requires specialized infrastructure:
• GPU clusters with efficient batch processing
• Model caching and warm-up strategies
• Automatic scaling based on inference latency
Reliability considerations
Enterprise deployments must account for:
1. Graceful degradation: When AI systems fail, fallback to human agents or simpler automated responses
2. Circuit breakers: Prevent cascading failures when external services become unavailable
3. Comprehensive observability: Tracing, metrics, and logging for debugging production issues
Enterprise deployments must account for:
1. Graceful degradation: When AI systems fail, fallback to human agents or simpler automated responses
2. Circuit breakers: Prevent cascading failures when external services become unavailable
3. Comprehensive observability: Tracing, metrics, and logging for debugging production issues